Passive IR sensitive (“PIR”) detectors for recognizing a presence are known, said detectors usually reacting in a differential manner to object movements in their field of view by way of a simple signal capture. Here, conventional PIR detectors usually use PIR sensors on the basis of pyroelectric effects, which only react to changing IR radiation. That is to say, a constant background radiation remains unconsidered. Such PIR sensors can only be used as motion detectors—in cooperation with Fresnel zone optics from a technical point of view—and cannot be used to detect a static presence. However, this is not sufficient for advanced object recognition and/or object classification, so-called because it at least also relates to static object recognition and/or object classification. A further disadvantage of the PIR detectors consists in the fact that these have a relatively large installation volume on account of their IR-capable Fresnel optics. Moreover, a relatively high false positive detection rate emerges on account of the typically low angular resolution and range. If the motion detector is activated within the scope of a lighting system, a person must render themselves noticeable by way of clear gestures on account of the pure movement sensitivity to ensure the lighting system is or remains activated.
A further group of known motion detectors includes active motion detectors which emit microwaves in the sub-GHz range or else ultrasonic waves in order to search for Doppler shifts of moving objects in their echoes. Such active motion detectors are typically also only used as motion detectors and not for the detection of a static presence.
Further, a camera-based presence recognition using a CMOS sensor is known. The CMOS sensor records images in the typically visible spectral range or captures corresponding image data. The CMOS sensor is usually coupled to a data processing device which processes the recorded images or image data in respect of a presence and classification of present objects.
For the purposes of an object recognition with CMOS sensors, it is known initially to release at least one object in the image or the image data from a general background, or subject said object to free-form selection, and subsequently analyze the object by means of a feature-based object recognition or pattern recognition and classify said object in respect of its properties and hence recognize it. For the presence recognition and general lighting fields of application, objects which are similar to a person or a human contour are mainly of interest, in order, for example, to emit a corresponding status signal to a light management system in the case of a positive result.
However, a direct arithmetic comparison between the captured object and the reference object cannot be used as the similarity measure within the scope of the image processing since, to this end, the two comparison images would have to have the same image values such as exposure, contrast, position, and perspective; in practice, this is not the case. Therefore, the normalized cross correlation analysis (also referred to as NCC) is often used in practice, in which an object which was subjected to free-form selection from the background and then captured or “segmented” is compared with a suitable reference image by way of statistical 2D cross correlation analyses and the result of the comparison is used as a characteristic similarity measure for making a decision about a presence of a person. The normalized cross correlation analysis to this end evaluates absolute differences between the captured object and the reference image using statistical methods, while absolute sums between the original image and the reference image may also still be evaluated using a convolution analysis in a complementary manner.
Since the normalized cross correlation analysis requires much computational and memory outlay, it is only suitable to a restricted extent for implementation in a microcontroller or DSP-based sensor system, which typically has restricted computational power and memory capacity.